Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM10
BOOL1

Warnings

X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2020-12-15 20:04:20.353397
Analysis finished2020-12-15 20:04:43.957657
Duration23.6 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0403644055
Minimum-3.24536605
Maximum3.174938727
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:44.055715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.24536605
5-th percentile-1.794935307
Q1-0.6939938296
median-0.0172247499
Q30.622162731
95-th percentile1.599991625
Maximum3.174938727
Range6.420304777
Interquartile range (IQR)1.316156561

Descriptive statistics

Standard deviation1.018309419
Coefficient of variation (CV)-25.22790579
Kurtosis0.01045963782
Mean-0.0403644055
Median Absolute Deviation (MAD)0.6510907074
Skewness-0.1049546095
Sum-40.3644055
Variance1.036954073
MonotocityNot monotonic
2020-12-15T21:04:44.271454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.285238890110.1%
 
0.154560152110.1%
 
-0.715048775710.1%
 
-1.2743594310.1%
 
-0.786714808710.1%
 
-0.9415936510.1%
 
-0.855513957310.1%
 
0.471052429110.1%
 
-0.242964635710.1%
 
0.328965984810.1%
 
0.639822473110.1%
 
0.528878841110.1%
 
-0.735387207310.1%
 
-0.00654006479410.1%
 
-1.21587475310.1%
 
0.197997021310.1%
 
1.56100702410.1%
 
-1.90661993910.1%
 
0.125549125110.1%
 
-0.818936043610.1%
 
-0.649476169810.1%
 
1.57429491710.1%
 
0.906413993310.1%
 
0.849222261610.1%
 
-0.0748060743610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.2453660510.1%
 
-3.0734928910.1%
 
-2.94128925710.1%
 
-2.88142386710.1%
 
-2.72359475410.1%
 
-2.63341897510.1%
 
-2.63317771410.1%
 
-2.61515976410.1%
 
-2.60197715110.1%
 
-2.56106798810.1%
 
ValueCountFrequency (%) 
3.17493872710.1%
 
2.71455105810.1%
 
2.63661375610.1%
 
2.56417019710.1%
 
2.5039456710.1%
 
2.38652841110.1%
 
2.33887532110.1%
 
2.31484996710.1%
 
2.2756831210.1%
 
2.27282083410.1%
 

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00386631708
Minimum-3.325521169
Maximum2.89833445
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:44.492560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.325521169
5-th percentile-1.757182536
Q1-0.6321624876
median0.07859654651
Q30.6618111214
95-th percentile1.556832665
Maximum2.89833445
Range6.223855619
Interquartile range (IQR)1.293973609

Descriptive statistics

Standard deviation0.9964756696
Coefficient of variation (CV)257.7325266
Kurtosis0.06462655354
Mean0.00386631708
Median Absolute Deviation (MAD)0.6490862879
Skewness-0.2093870368
Sum3.86631708
Variance0.9929637601
MonotocityNot monotonic
2020-12-15T21:04:44.690236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.407872622110.1%
 
-1.22695734110.1%
 
-1.88413728610.1%
 
0.501278807410.1%
 
0.484936908210.1%
 
-0.0892649775510.1%
 
-0.783630067310.1%
 
0.267166432710.1%
 
0.346670357310.1%
 
0.59938390710.1%
 
0.670810610110.1%
 
-0.783612341110.1%
 
-0.230710242310.1%
 
-0.436700173410.1%
 
-0.608921060810.1%
 
2.52398596410.1%
 
0.418190103910.1%
 
0.526657308210.1%
 
0.602569778910.1%
 
0.536013958510.1%
 
-0.276015360310.1%
 
-0.641490128710.1%
 
-0.0475549256610.1%
 
0.384664828510.1%
 
-0.607953095310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.32552116910.1%
 
-2.88274795910.1%
 
-2.85120121410.1%
 
-2.80185986510.1%
 
-2.70698133310.1%
 
-2.63272363510.1%
 
-2.59982191810.1%
 
-2.58677826510.1%
 
-2.57418874410.1%
 
-2.52913507410.1%
 
ValueCountFrequency (%) 
2.8983344510.1%
 
2.85144647110.1%
 
2.64853299710.1%
 
2.61549796610.1%
 
2.52398596410.1%
 
2.50083963410.1%
 
2.44498141610.1%
 
2.44097580810.1%
 
2.41481991110.1%
 
2.36502749810.1%
 

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01742363582
Minimum-3.170566469
Maximum3.308664177
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:44.901438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.170566469
5-th percentile-1.620621196
Q1-0.6854056877
median0.009984648376
Q30.660320513
95-th percentile1.655806967
Maximum3.308664177
Range6.479230647
Interquartile range (IQR)1.345726201

Descriptive statistics

Standard deviation1.002868647
Coefficient of variation (CV)57.55794355
Kurtosis-0.0810080648
Mean0.01742363582
Median Absolute Deviation (MAD)0.6864154486
Skewness0.02696372783
Sum17.42363582
Variance1.005745523
MonotocityNot monotonic
2020-12-15T21:04:45.100504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.712417935410.1%
 
-0.00854623981410.1%
 
-0.605130394610.1%
 
0.248099513510.1%
 
1.43069034910.1%
 
-0.666135884210.1%
 
0.73869346210.1%
 
1.56704592510.1%
 
-1.23578559710.1%
 
-2.60430132110.1%
 
-1.42057905810.1%
 
0.334521564710.1%
 
0.67724985110.1%
 
1.16836771910.1%
 
-0.0473224508610.1%
 
1.85594637810.1%
 
1.44261243610.1%
 
-1.10796144710.1%
 
0.441173786210.1%
 
1.86456715710.1%
 
0.295926410910.1%
 
-0.931553551710.1%
 
1.25476807610.1%
 
-1.40867213810.1%
 
-0.0372486341110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.17056646910.1%
 
-2.81473955110.1%
 
-2.70974409310.1%
 
-2.62875036910.1%
 
-2.60430132110.1%
 
-2.54509429210.1%
 
-2.50941348310.1%
 
-2.44098135310.1%
 
-2.3119511910.1%
 
-2.30771668410.1%
 
ValueCountFrequency (%) 
3.30866417710.1%
 
3.17215649610.1%
 
3.05492493410.1%
 
2.90535993310.1%
 
2.61999934910.1%
 
2.52372131710.1%
 
2.44065206910.1%
 
2.26742137610.1%
 
2.25495972910.1%
 
2.24814700910.1%
 

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.06954780384
Minimum-3.401992293
Maximum2.934810723
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:45.455814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.401992293
5-th percentile-1.695425622
Q1-0.7260832989
median-0.08176472779
Q30.5897032643
95-th percentile1.659873735
Maximum2.934810723
Range6.336803016
Interquartile range (IQR)1.315786563

Descriptive statistics

Standard deviation1.020763272
Coefficient of variation (CV)-14.67714601
Kurtosis0.08989699156
Mean-0.06954780384
Median Absolute Deviation (MAD)0.6538904237
Skewness0.005051161313
Sum-69.54780384
Variance1.041957657
MonotocityNot monotonic
2020-12-15T21:04:45.668933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.799959633910.1%
 
0.744896777410.1%
 
-0.138448337710.1%
 
-0.287385038210.1%
 
-1.14415238210.1%
 
-0.983003223510.1%
 
0.335152302910.1%
 
0.142909016110.1%
 
-0.00369298510.1%
 
-1.85633824310.1%
 
-0.0858839109510.1%
 
0.254440183910.1%
 
0.381334361710.1%
 
0.561532773210.1%
 
-0.510845543610.1%
 
0.00603575712510.1%
 
-0.517441139810.1%
 
2.06468054210.1%
 
0.0471972081210.1%
 
-0.654964411610.1%
 
-0.180665620410.1%
 
0.37162869110.1%
 
0.521328675410.1%
 
0.475304270310.1%
 
-0.336169831710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.40199229310.1%
 
-3.09941943710.1%
 
-3.03435124410.1%
 
-2.98606604810.1%
 
-2.93075899410.1%
 
-2.92220757610.1%
 
-2.81305478410.1%
 
-2.73383138110.1%
 
-2.61478445610.1%
 
-2.577518710.1%
 
ValueCountFrequency (%) 
2.93481072310.1%
 
2.86537286810.1%
 
2.69350685910.1%
 
2.65784653310.1%
 
2.58441310510.1%
 
2.58186856510.1%
 
2.57082034610.1%
 
2.53571666210.1%
 
2.34724041810.1%
 
2.29860351310.1%
 

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0193133549
Minimum-3.668117642
Maximum3.364675786
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:45.903230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.668117642
5-th percentile-1.680888096
Q1-0.6736513186
median0.003752816893
Q30.6133539308
95-th percentile1.584770483
Maximum3.364675786
Range7.032793429
Interquartile range (IQR)1.287005249

Descriptive statistics

Standard deviation0.9814132612
Coefficient of variation (CV)-50.81526573
Kurtosis0.2484711907
Mean-0.0193133549
Median Absolute Deviation (MAD)0.6482574926
Skewness-0.08598122895
Sum-19.3133549
Variance0.9631719893
MonotocityNot monotonic
2020-12-15T21:04:46.124619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.038204435710.1%
 
-1.20137320610.1%
 
0.747175982810.1%
 
0.168531441310.1%
 
-0.0488955021610.1%
 
-0.110004268510.1%
 
0.168353455710.1%
 
-1.10572687610.1%
 
0.20752404610.1%
 
0.77658311910.1%
 
0.493117649510.1%
 
-1.41169988810.1%
 
-1.95520551310.1%
 
0.574663130210.1%
 
-0.676710434710.1%
 
1.05908428610.1%
 
-0.0232891352510.1%
 
0.399698891310.1%
 
0.716385883310.1%
 
-0.981159356510.1%
 
1.34718495710.1%
 
0.79183637810.1%
 
0.145433244710.1%
 
-0.414870994110.1%
 
-0.87779432710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.66811764210.1%
 
-3.4868447710.1%
 
-3.0635278610.1%
 
-2.68744926610.1%
 
-2.67017559710.1%
 
-2.59513154810.1%
 
-2.56972249210.1%
 
-2.54245324110.1%
 
-2.50605080710.1%
 
-2.37818282810.1%
 
ValueCountFrequency (%) 
3.36467578610.1%
 
2.71093194310.1%
 
2.62937378210.1%
 
2.52271416210.1%
 
2.50424551910.1%
 
2.4803532110.1%
 
2.44434774810.1%
 
2.41330097810.1%
 
2.2956784310.1%
 
2.28859991310.1%
 

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05034271391
Minimum-2.788231452
Maximum3.216233831
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:46.352699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.788231452
5-th percentile-1.523953065
Q1-0.6051667941
median0.02044956398
Q30.6815711011
95-th percentile1.62398232
Maximum3.216233831
Range6.004465283
Interquartile range (IQR)1.286737895

Descriptive statistics

Standard deviation0.9785051518
Coefficient of variation (CV)19.43687727
Kurtosis0.04847947073
Mean0.05034271391
Median Absolute Deviation (MAD)0.6417404161
Skewness0.08880942248
Sum50.34271391
Variance0.9574723321
MonotocityNot monotonic
2020-12-15T21:04:46.563076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.888159422810.1%
 
0.452185244710.1%
 
1.78783352410.1%
 
-1.27931535410.1%
 
-0.698301364310.1%
 
-0.0526859809310.1%
 
-0.217016739910.1%
 
-0.0567488392610.1%
 
1.60275780610.1%
 
-0.36773478810.1%
 
0.681126513910.1%
 
-0.134681646210.1%
 
-1.02252503910.1%
 
-1.28580506110.1%
 
-0.352216454810.1%
 
1.60470491310.1%
 
0.287809069910.1%
 
1.52025351810.1%
 
1.58752238810.1%
 
-0.25911435210.1%
 
0.680420189310.1%
 
-0.196153322710.1%
 
-0.191893473810.1%
 
0.90179329810.1%
 
1.77068585910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.78823145210.1%
 
-2.66502380310.1%
 
-2.62579333710.1%
 
-2.58516764610.1%
 
-2.43462556810.1%
 
-2.42751354910.1%
 
-2.42304245710.1%
 
-2.33015025410.1%
 
-2.28650761410.1%
 
-2.26829620410.1%
 
ValueCountFrequency (%) 
3.21623383110.1%
 
3.16448161910.1%
 
2.98772853810.1%
 
2.91346614710.1%
 
2.90205281810.1%
 
2.85156901910.1%
 
2.73091345710.1%
 
2.71067699710.1%
 
2.56700583510.1%
 
2.53388955610.1%
 

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007590104413
Minimum-3.185081497
Maximum3.282257164
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:46.788820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.185081497
5-th percentile-1.733758925
Q1-0.6870854352
median0.01260751132
Q30.6922511299
95-th percentile1.713636173
Maximum3.282257164
Range6.467338661
Interquartile range (IQR)1.379336565

Descriptive statistics

Standard deviation1.040556924
Coefficient of variation (CV)137.0938879
Kurtosis0.000874378265
Mean0.007590104413
Median Absolute Deviation (MAD)0.6939843306
Skewness0.06069363945
Sum7.590104413
Variance1.082758712
MonotocityNot monotonic
2020-12-15T21:04:46.998189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.501016204510.1%
 
0.953680440610.1%
 
0.839854931910.1%
 
-0.744672509110.1%
 
0.0455759197410.1%
 
0.858254210210.1%
 
-0.22087411210.1%
 
0.159270209610.1%
 
-0.0124318461610.1%
 
-0.515293568110.1%
 
-2.57187612810.1%
 
1.95650407910.1%
 
0.00722593969210.1%
 
-0.0296367119210.1%
 
-0.994363718310.1%
 
0.891963042910.1%
 
0.176747721810.1%
 
-0.226706319210.1%
 
-0.00437882735910.1%
 
0.494220354410.1%
 
-0.654374075210.1%
 
0.428402886210.1%
 
0.413752209110.1%
 
-1.86115388810.1%
 
2.20640651110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.18508149710.1%
 
-2.9909464310.1%
 
-2.85456792710.1%
 
-2.76302753110.1%
 
-2.75560725710.1%
 
-2.59315066310.1%
 
-2.57187612810.1%
 
-2.56771878910.1%
 
-2.52775068310.1%
 
-2.43878221210.1%
 
ValueCountFrequency (%) 
3.28225716410.1%
 
3.12918410910.1%
 
3.05461662810.1%
 
2.81089502810.1%
 
2.79726185310.1%
 
2.67949995710.1%
 
2.6701679910.1%
 
2.63212936710.1%
 
2.56419670210.1%
 
2.54247253710.1%
 

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03625307248
Minimum-2.860270442
Maximum3.149857452
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:47.223321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.860270442
5-th percentile-1.650260351
Q1-0.6985350349
median-0.03891161356
Q30.610472285
95-th percentile1.669813011
Maximum3.149857452
Range6.010127894
Interquartile range (IQR)1.30900732

Descriptive statistics

Standard deviation1.01089073
Coefficient of variation (CV)-27.88427741
Kurtosis0.01705462159
Mean-0.03625307248
Median Absolute Deviation (MAD)0.6591570664
Skewness0.148922757
Sum-36.25307248
Variance1.021900068
MonotocityNot monotonic
2020-12-15T21:04:47.431093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.543901083810.1%
 
-0.91464270310.1%
 
0.906026211710.1%
 
1.28014166310.1%
 
-0.226564719510.1%
 
1.60640768110.1%
 
-1.49048119310.1%
 
-0.149994761310.1%
 
-0.728194423210.1%
 
0.870813594210.1%
 
0.503959687710.1%
 
0.134417623410.1%
 
0.839572198610.1%
 
0.0855698752210.1%
 
0.089236040410.1%
 
0.447053069110.1%
 
-0.963770465210.1%
 
-0.936842429710.1%
 
-1.56219341610.1%
 
0.372683426610.1%
 
1.28596604410.1%
 
-0.424278700910.1%
 
-0.0930415714110.1%
 
-0.528406144110.1%
 
1.12343987810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.86027044210.1%
 
-2.74977284110.1%
 
-2.69141414210.1%
 
-2.62159453610.1%
 
-2.51950307410.1%
 
-2.32873153910.1%
 
-2.32117380210.1%
 
-2.2918001810.1%
 
-2.27231993210.1%
 
-2.26467369110.1%
 
ValueCountFrequency (%) 
3.14985745210.1%
 
3.07977904310.1%
 
2.96071619710.1%
 
2.82546404110.1%
 
2.73621634110.1%
 
2.7114911110.1%
 
2.6928182510.1%
 
2.66496827210.1%
 
2.65120386310.1%
 
2.49701880310.1%
 

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003859921933
Minimum-3.78834231
Maximum3.600723821
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:47.666810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.78834231
5-th percentile-1.679335171
Q1-0.6582695296
median0.06559124407
Q30.6624970656
95-th percentile1.678103024
Maximum3.600723821
Range7.389066131
Interquartile range (IQR)1.320766595

Descriptive statistics

Standard deviation1.032069904
Coefficient of variation (CV)267.3810305
Kurtosis0.3331986807
Mean0.003859921933
Median Absolute Deviation (MAD)0.6639508944
Skewness-0.1561327963
Sum3.859921933
Variance1.065168287
MonotocityNot monotonic
2020-12-15T21:04:47.891868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.403076701410.1%
 
-0.322317837310.1%
 
-0.132695049810.1%
 
-0.155499162110.1%
 
-1.79301341910.1%
 
0.884808042910.1%
 
0.301727014410.1%
 
1.10686603110.1%
 
0.404238196110.1%
 
-0.852733471510.1%
 
1.0677279410.1%
 
1.34215010910.1%
 
-0.574949979310.1%
 
2.21860901710.1%
 
0.0374205882610.1%
 
1.0185784810.1%
 
-1.059411610.1%
 
0.818474969310.1%
 
-1.03270237710.1%
 
2.05309755310.1%
 
1.05944330810.1%
 
-0.59638194210.1%
 
-0.0952096131710.1%
 
0.746688876610.1%
 
0.626205024310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.7883423110.1%
 
-3.57803716410.1%
 
-3.502804810.1%
 
-3.25632283310.1%
 
-3.15527495610.1%
 
-2.99433887610.1%
 
-2.94831174610.1%
 
-2.90295793810.1%
 
-2.87436601410.1%
 
-2.70155848110.1%
 
ValueCountFrequency (%) 
3.60072382110.1%
 
2.80990566610.1%
 
2.71127972510.1%
 
2.67319582610.1%
 
2.63470792510.1%
 
2.55577467910.1%
 
2.51648971210.1%
 
2.51552079610.1%
 
2.45666747810.1%
 
2.43845042210.1%
 

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02080367011
Minimum-3.205085052
Maximum3.264380567
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T21:04:48.127782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.205085052
5-th percentile-1.673919493
Q1-0.6703334736
median0.04523507387
Q30.7153451973
95-th percentile1.675228106
Maximum3.264380567
Range6.469465619
Interquartile range (IQR)1.385678671

Descriptive statistics

Standard deviation1.011345152
Coefficient of variation (CV)48.61378528
Kurtosis0.05369875681
Mean0.02080367011
Median Absolute Deviation (MAD)0.6991991844
Skewness-0.06757436076
Sum20.80367011
Variance1.022819016
MonotocityNot monotonic
2020-12-15T21:04:48.473703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.01769976110.1%
 
0.349797607210.1%
 
-0.081430585610.1%
 
-0.122378246510.1%
 
-0.0922126122310.1%
 
-0.916011635310.1%
 
-0.210012559710.1%
 
0.422304004510.1%
 
-0.86267807910.1%
 
0.362776217710.1%
 
0.0404268788810.1%
 
-0.906950367710.1%
 
0.922389338910.1%
 
0.880943058510.1%
 
1.26248507910.1%
 
0.819888715110.1%
 
-0.551521693210.1%
 
1.07317586210.1%
 
-0.419608379810.1%
 
0.886188269710.1%
 
-1.35177267610.1%
 
-0.466486024110.1%
 
-0.423409352610.1%
 
-0.968765606410.1%
 
0.370818579710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.20508505210.1%
 
-3.02519057410.1%
 
-2.98300070510.1%
 
-2.86768004510.1%
 
-2.65084074210.1%
 
-2.62039375310.1%
 
-2.56488274810.1%
 
-2.4464895210.1%
 
-2.4071862910.1%
 
-2.40371091510.1%
 
ValueCountFrequency (%) 
3.26438056710.1%
 
2.88701739910.1%
 
2.85064871510.1%
 
2.73105673910.1%
 
2.60145317710.1%
 
2.5593943910.1%
 
2.53987166510.1%
 
2.46870811910.1%
 
2.35638952710.1%
 
2.33798877210.1%
 

y
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
500 
0
500 
ValueCountFrequency (%) 
150050.0%
 
050050.0%
 
2020-12-15T21:04:48.635039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-15T21:04:21.145772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:21.358188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:21.578161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:21.788326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:22.136037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:22.361198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:22.576095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:22.787598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:23.001858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:23.218443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:23.417122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:23.624088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:23.838180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:24.044141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:24.256782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:24.461462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:24.663667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:24.867642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:25.074156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:25.281735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:25.486456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:25.700286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:25.915779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:26.129222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:26.339337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:26.553804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:26.773344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:26.986282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:27.195409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:27.411042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:27.617268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:27.825663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:28.037426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:28.390713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:28.608805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:28.831552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:29.048120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:29.266018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:29.489893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:29.712724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:29.921450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:30.143413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:30.361529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:30.592686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:30.813215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:31.040632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:31.255688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:31.481215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:31.684184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:31.903575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:32.130450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:32.340341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:32.552754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:32.765827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:32.981812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:33.190185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:33.401775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:33.612013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:33.826163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:34.050775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:34.263128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:34.614226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:34.830056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:35.049190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:35.265253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:35.483644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:35.701356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:35.911469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:36.126085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:36.346264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:36.567751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:36.784456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:36.989094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:37.205067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:37.416543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:37.627882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:37.839457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:38.074127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:38.458502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:38.675117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:38.890152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:39.112358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:39.335173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:39.547896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:39.776936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:40.003503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:40.220201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:40.434695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:40.647567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:41.000629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:41.213499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:41.422523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:41.622585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:41.827984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:42.036386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:42.255755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:42.469128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:42.678037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:42.890999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:43.110073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-15T21:04:48.750556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-15T21:04:49.038541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-15T21:04:49.326184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-15T21:04:49.632249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-15T21:04:43.459023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T21:04:43.817703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

X0X1X2X3X4X5X6X7X8X9y
0-0.443250-0.3420361.373908-0.0705310.5641300.925309-0.3103330.8908531.039581-0.6697411
11.2300121.1043051.4083061.5638000.730108-1.051826-1.6482371.2655251.1081281.3426701
2-0.626974-0.2210730.599768-0.8149400.947891-0.193940-1.092746-0.914643-0.3046260.7806220
3-0.2033561.102541-1.6956120.630432-0.853570-0.097504-1.8784360.0887381.0677280.0180091
40.414291-0.6596561.6353420.5070620.1296840.661946-2.0124101.403161-0.4458560.6236761
51.4678190.0758530.315528-0.517441-0.510529-1.116447-0.6632950.7733240.3735120.4848471
6-1.9320711.907612-0.8462161.6696710.6615270.420374-0.496145-1.552295-1.5045180.1421130
7-0.736341-0.089265-0.3183130.572952-1.295395-0.6589780.3737170.6908620.7232381.8279631
8-0.443058-0.605626-1.1091950.0103650.5835210.823901-1.0481690.9937500.630946-0.1230711
9-1.002351-0.888541-0.5405720.085350-1.221651-0.865834-0.0565960.644671-0.8908111.9279820

Last rows

X0X1X2X3X4X5X6X7X8X9y
990-0.864608-0.691314-1.0561810.309340-2.5951320.1749820.3144260.6267160.824230-1.2013180
9910.450615-0.425939-1.1593000.1429090.574663-0.5747040.3176590.7092930.486560-0.2757141
992-1.1240200.6345911.855946-1.6433511.564701-0.6324160.7271030.428797-1.747969-1.3606270
993-1.6747650.347229-0.306622-0.8260710.173122-0.0954920.210708-0.878807-0.512205-2.3489560
9941.068888-2.0703000.589843-0.6368230.657433-1.9990200.935144-1.042555-1.315338-0.5724151
9950.2624330.584710-0.0727320.431633-0.689274-0.4818711.8558140.435476-0.081426-1.1047020
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